In this work, we use a multi-task approach with complementary contour-based tasks for rich and robust feature extraction and address the problem of spatial precision loss.
We work specifically with hourglass (encoder-decoder) models because we (empirically) discovered that a multi-task setup helps to adjust the latent space in these models (i.e., it exhibits a clustering behavior).
We also show that the improved results of different hourglass (encoder-decoder) architectures are directly related to this clustering behavior.
We present four different studies on the latent space for SS:
(i) visualization of the latent space behavior,
(ii) activation maps used by the models to predict the segmentation,
(iii) reduction of over-fitting of the models, and
(iv) the ablation studies on loss functions and complementary tasks.
The latent space's visualizations show different induced clusters when influenced by adding or removing the different complementary contour-based tasks.
Fig. REF  depicts the contour-based tasks used in our study.
Namely, they are edge detection [1]}, semantic contours [2]}, semantic segmentation [3]}, and distance transform [4]}.
